Fracture behaviour prediction of materials is a critical aspect in various industries, including aerospace, automotive, and manufacturing. Accurate prediction of fracture behaviour can aid in designing robust materials and structures, enhancing safety, and optimizing performance. In this study, we propose the development of a machine learning model for predicting fracture behaviour of materials using artificial intelligence (AI) techniques. The methodology involves the collection and pre-processing of a comprehensive dataset comprising material properties, structural characteristics, and fracture behaviour observations. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, are employed to train and optimize the predictive model. Feature engineering techniques are utilized to extract relevant features and reduce dimensionality. The model's performance is evaluated using appropriate metrics, including accuracy, precision, recall, and F1-score. The significance lies in the potential to provide accurate and efficient predictions of fracture behaviour, thereby enabling informed decision-making in material selection, design, and performance optimization. By leveraging AI techniques, we aim to overcome the limitations of traditional fracture prediction methods that rely on empirical models or complex numerical simulations. Developing a machine learning model for fracture behaviour prediction, evaluating the performance of different algorithms and feature engineering techniques, and assessing the practical implications and benefits of the developed model in real-world applications. Introducing a novel approach to predicting fracture behaviour using AI techniques. The results of this study have the potential to enhance the understanding of material fracture mechanisms and pave the way for improved material design and performance optimization.